{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/countVctorRidgeclassification/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from transformers import BertTokenizer, BertForSequenceClassification, get_scheduler\n",
    "from datasets import load_dataset,Dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from torch.optim import AdamW\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 记载数据\n",
    "df_train = pd.read_csv('/root/data/train_set.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 数据预处理\n",
    "## 2.1 数据通过bert模型进行向量化\n",
    "train_dataset = Dataset.from_pandas(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset['label'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('/root/model/bert-base-chinese')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples['text'], truncation=True, padding=True, max_length=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 200000/200000 [37:22<00:00, 89.20 examples/s]\n"
     ]
    }
   ],
   "source": [
    "encoded_dataset = train_dataset.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_torch = encoded_dataset.with_format('torch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  101, 11545,  8161,  8369,  9265, 11863,  9960,  9560,  8159, 12779,\n",
       "         8148, 10706,  8160, 12613,  8144, 12815,  8160,  9564,  8160,  9860,\n",
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       "         8152, 11210,  8158,  8460,  8158, 13068,  8158,  8284,  8161,  8567,\n",
       "        13262,  8203,  9292, 11256,  8129, 11545,  8161,   102])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset_torch['input_ids'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.2 转换为dotaloader\n",
    "train_loader = DataLoader(train_dataset_torch, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataloader.DataLoader at 0x7fb0e8a16fb0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_labels = len(set(df_train['label']))  # 假设标签是从 0 开始的连续整数\n",
    "num_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at /root/model/bert-base-chinese and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# 3. 加载模型\n",
    "model = BertForSequenceClassification.from_pretrained('/root/model/bert-base-chinese', num_labels=num_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 优化器\n",
    "optimizer = AdamW(model.parameters(), lr=3e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_training_steps = len(train_loader) * 3  # 假设训练 3 个 epoch\n",
    "lr_scheduler = get_scheduler(\"linear\", optimizer=optimizer, num_warmup_steps=num_training_steps * 0.1, num_training_steps=num_training_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(21128, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSdpaSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=14, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设备配置\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
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    {
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      "Loss: 0.18802164494991302\n",
      "Loss: 0.05803898721933365\n",
      "Loss: 0.09727435559034348\n",
      "Epoch 3 completed.\n"
     ]
    }
   ],
   "source": [
    "# 4. 训练模型\n",
    "model.train()\n",
    "for epoch in range(3):  # 训练 3 个 epoch\n",
    "    for i , batch in enumerate(train_loader):\n",
    "        # print(batch)  'target':batch['label'].to(device), \n",
    "        outputs = model(**{'input_ids':batch['input_ids'].to(device),'token_type_ids':batch['token_type_ids'].to(device),'attention_mask':batch['attention_mask'].to(device)})\n",
    "        # print(outputs)\n",
    "        logits = outputs.logits\n",
    "\n",
    "        # 如果需要计算损失（在训练时）\n",
    "        loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "        loss = loss_fn(logits, batch['label'].to(device))\n",
    "         \n",
    "        # print(\"Logits:\", logits)\n",
    "        if i %10 == 0: \n",
    "            print(\"Loss:\", loss.item()) \n",
    "        \n",
    "        loss.backward()\n",
    " \n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "    print(\"Loss:\", loss.item()) \n",
    "    print(f\"Epoch {epoch + 1} completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 模型存储\n",
    "torch.save(model.state_dict(), 'BertForSequenceClassification.pth')"
   ]
  }
 ],
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